Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
bert
mteb
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use llmrails/ember-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use llmrails/ember-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("llmrails/ember-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use llmrails/ember-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="llmrails/ember-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1") model = AutoModel.from_pretrained("llmrails/ember-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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### Limitation
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This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
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This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
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<img src="https://pixel.llmrails.com/hf/2AtscRthisA1rZzQr8T7Zm">
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